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This article explores the research question: ‘What are ChatGPT’s human-like traits as perceived by society?’ Thematic analyses of insights from 452 individuals worldwide yielded two categories of traits. Category 1 entails social traits, where ChatGPT embodies the social roles of ‘author’ (imitating human phrasing and paraphrasing practices) and ‘interactor’ (simulating human collaboration and emotion). Category 2 encompasses political traits, with ChatGPT assuming the political roles of ‘agent’ (emulating human cognition and identity) and ‘influencer’ (mimicking human diplomacy and consultation). When asked, ChatGPT confirmed the possession of these human-like traits (except for one trait). Thus, ChatGPT displays human-like qualities, humanising itself through the ‘game of algorithms’.

These stylistic choices make the Aṣṭādhyāyī shorter and easier to memorize than it would be otherwise — some historians believe it was initially composed orally — but also incredibly dense. That density leads to rule conflicts, in which two rules may apply simultaneously to the same word yet produce different outcomes.

Pāṇini did provide a meta-rule to solve such conflicts. According to traditional scholarship, this meta-rule states that “in the event of a conflict between two rules of equal strength, the rule that comes later in the serial order of the Aṣṭādhyāyī wins.”

Seems simple enough. But when applied, this meta-rule yields many exceptions. To correct those exceptions, scholars have for centuries created their own meta-rules. However, those meta-rules yielded even more exceptions, which required the creation of additional meta-rules (meta-meta-rules?). Those meta-rules in turn created even more exceptions — and you see where this is going.

AI can also help develop objective risk stratification scores, predict the course of disease or treatment outcomes in CLD or liver cancer, facilitate easier and more successful liver transplantation, and develop quality metrics for hepatology.

Artificial Intelligence (AI) is an umbrella term that covers all computational processes aimed at mimicking and extending human intelligence for problem-solving and decision-making. It is based on algorithms or arrays of mathematical formulae that make up specific computational learning methods. Machine learning (ML) and deep learning (DL) use algorithms in more complex ways to predict learned and new outcomes.

AI-powered liver disease diagnosis Machine learning for treatment planning Predicting disease progression The future of hepatology References Further reading

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Effective compression is about finding patterns to make data smaller without losing information. When an algorithm or model can accurately guess the next piece of data in a sequence, it shows it’s good at spotting these patterns. This links the idea of making good guesses—which is what large language models like GPT-4 do very well —to achieving good compression.

In an arXiv research paper titled “Language Modeling Is Compression,” researchers detail their discovery that the DeepMind large language model (LLM) called Chinchilla 70B can perform lossless compression on image patches from the ImageNet image database to 43.4 percent of their original size, beating the PNG algorithm, which compressed the same data to 58.5 percent. For audio, Chinchilla compressed samples from the LibriSpeech audio data set to just 16.4 percent of their raw size, outdoing FLAC compression at 30.3 percent.

In this case, lower numbers in the results mean more compression is taking place. And lossless compression means that no data is lost during the compression process. It stands in contrast to a lossy compression technique like JPEG, which sheds some data and reconstructs some of the data with approximations during the decoding process to significantly reduce file sizes.

We often believe computers are more efficient than humans. After all, computers can complete a complex math equation in a moment and can also recall the name of that one actor we keep forgetting. However, human brains can process complicated layers of information quickly, accurately, and with almost no energy input: recognizing a face after only seeing it once or instantly knowing the difference between a mountain and the ocean. These simple human tasks require enormous processing and energy input from computers, and even then, with varying degrees of accuracy.

Creating brain-like computers with minimal energy requirements would revolutionize nearly every aspect of modern life. Funded by the Department of Energy, Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) — a nationwide consortium led by the University of California San Diego — has been at the forefront of this research.

UC San Diego Assistant Professor of Physics Alex Frañó is co-director of Q-MEEN-C and thinks of the center’s work in phases. In the first phase, he worked closely with President Emeritus of University of California and Professor of Physics Robert Dynes, as well as Rutgers Professor of Engineering Shriram Ramanathan. Together, their teams were successful in finding ways to create or mimic the properties of a single brain element (such as a neuron or synapse) in a quantum material.

A complete quantum computing system could be as large as a two-car garage when one factors in all the paraphernalia required for smooth operation. But the entire processing unit, made of qubits, would barely cover the tip of your finger.

Today’s smartphones, laptops and supercomputers contain billions of tiny electronic processing elements called transistors that are either switched on or off, signifying a 1 or 0, the binary language computers use to express and calculate all information. Qubits are essentially quantum transistors. They can exist in two well-defined states—say, up and down—which represent the 1 and 0. But they can also occupy both of those states at the same time, which adds to their computing prowess. And two—or more—qubits can be entangled, a strange quantum phenomenon where particles’ states correlate even if the particles lie across the universe from each other. This ability completely changes how computations can be carried out, and it is part of what makes quantum computers so powerful, says Nathalie de Leon, a quantum physicist at Princeton University. Furthermore, simply observing a qubit can change its behavior, a feature that de Leon says might create even more of a quantum benefit. “Qubits are pretty strange. But we can exploit that strangeness to develop new kinds of algorithms that do things classical computers can’t do,” she says.

Scientists are trying a variety of materials to make qubits. They range from nanosized crystals to defects in diamond to particles that are their own antiparticles. Each comes with pros and cons. “It’s too early to call which one is the best,” says Marina Radulaski of the University of California, Davis. De Leon agrees. Let’s take a look.

Today, we are living in the midst of a race to develop a quantum computer, one that could be used for practical applications. This device, built on the principles of quantum mechanics, holds the potential to perform computing tasks far beyond the capabilities of today’s fastest supercomputers. Quantum computers and other quantum-enabled technologies could foster significant advances in areas such as cybersecurity and molecular simulation, impacting and even revolutionizing fields such as online security, drug discovery and material fabrication.

An offshoot of this technological race is building what is known in scientific and engineering circles as a “quantum simulator”—a special type of quantum computer, constructed to solve one equation model for a specific purpose beyond the computing power of a standard computer. For example, in medical research, a quantum simulator could theoretically be built to help scientists simulate a specific, complex molecular interaction for closer study, deepening scientific understanding and speeding up drug development.

But just like building a practical, usable quantum computer, constructing a useful quantum simulator has proven to be a daunting challenge. The idea was first proposed by mathematician Yuri Manin in 1980. Since then, researchers have attempted to employ trapped ions, cold atoms and superconducting qubits to build a quantum simulator capable of real-world applications, but to date, these methods are all still a work in progress.

A new AI method can distinguish between biotic and abiotic samples with 90% accuracy.

In a press release, Bujack, who creates scientific visualizations at Los Alamos National Laboratory, called the current mathematical models used for color perceptions incorrect and requiring a “paradigm shift.”

A surprise finding

Being able to accurately model human color perception has a tremendous impact on automating image processing, computer graphics, and visualization. Bujack’s team first set out to develop algorithms that would automatically enhance color maps used in data visualization to make it easier to read them.